{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c08c6a31",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8b4d525d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 载入数据\n",
    "data = np.genfromtxt(\"job.csv\",delimiter=',')\n",
    "x_data = data[1:,1]\n",
    "y_data = data[1:,2]\n",
    "plt.scatter(x_data,y_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "61ab1503",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fc7c2485",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data = x_data[:,np.newaxis]\n",
    "y_data = y_data[:,np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "973b64c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.],\n",
       "       [ 2.],\n",
       "       [ 3.],\n",
       "       [ 4.],\n",
       "       [ 5.],\n",
       "       [ 6.],\n",
       "       [ 7.],\n",
       "       [ 8.],\n",
       "       [ 9.],\n",
       "       [10.]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "784c8060",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建并拟合模型\n",
    "model = LinearRegression()\n",
    "model.fit(x_data,y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8594246c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画图\n",
    "plt.plot(x_data,y_data,'b.')\n",
    "plt.plot(x_data,model.predict(x_data),'r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "72c9d622",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 定义多项式回归，degree的值可以调节多项式的特征\n",
    "poly_reg = PolynomialFeatures(degree=5)\n",
    "# 特征处理\n",
    "x_poly = poly_reg.fit_transform(x_data)\n",
    "# 定义回归模型\n",
    "lin_reg = LinearRegression()\n",
    "# 训练模型\n",
    "lin_reg.fit(x_poly,y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "51fd42e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00,\n",
       "        1.0000e+00],\n",
       "       [1.0000e+00, 2.0000e+00, 4.0000e+00, 8.0000e+00, 1.6000e+01,\n",
       "        3.2000e+01],\n",
       "       [1.0000e+00, 3.0000e+00, 9.0000e+00, 2.7000e+01, 8.1000e+01,\n",
       "        2.4300e+02],\n",
       "       [1.0000e+00, 4.0000e+00, 1.6000e+01, 6.4000e+01, 2.5600e+02,\n",
       "        1.0240e+03],\n",
       "       [1.0000e+00, 5.0000e+00, 2.5000e+01, 1.2500e+02, 6.2500e+02,\n",
       "        3.1250e+03],\n",
       "       [1.0000e+00, 6.0000e+00, 3.6000e+01, 2.1600e+02, 1.2960e+03,\n",
       "        7.7760e+03],\n",
       "       [1.0000e+00, 7.0000e+00, 4.9000e+01, 3.4300e+02, 2.4010e+03,\n",
       "        1.6807e+04],\n",
       "       [1.0000e+00, 8.0000e+00, 6.4000e+01, 5.1200e+02, 4.0960e+03,\n",
       "        3.2768e+04],\n",
       "       [1.0000e+00, 9.0000e+00, 8.1000e+01, 7.2900e+02, 6.5610e+03,\n",
       "        5.9049e+04],\n",
       "       [1.0000e+00, 1.0000e+01, 1.0000e+02, 1.0000e+03, 1.0000e+04,\n",
       "        1.0000e+05]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_poly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "54b654c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画图\n",
    "plt.plot(x_data,y_data,'b.')\n",
    "plt.plot(x_data,lin_reg.predict(poly_reg.fit_transform(x_data)),c='r')\n",
    "plt.title('Truth of Bluff(Polynomial Regression)')\n",
    "plt.xlabel('Position level')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cab9edb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画图\n",
    "plt.plot(x_data,y_data,'b.')\n",
    "x_test = np.linspace(1,10,100)\n",
    "x_test = x_test[:,np.newaxis]\n",
    "plt.plot(x_test,lin_reg.predict(poly_reg.fit_transform(x_test)),c='r')\n",
    "plt.title('Truth of Bluff(Polynomial Regression)')\n",
    "plt.xlabel('Position level')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
